Abstract
The analysis of ship radiation signals to identify ships is an important research content of underwater acoustic signal processing. The traditional fast Fourier transform (FFT) is not suitable for analyzing non-stationary, non-Gaussian, and nonlinear signal processing. In order to realize the feature extraction and accurate classification of ship radiation signals with higher accuracy, a feature extraction method of ship radiation signals based on wavelet packet decomposition and energy entropy is proposed in this paper. According to wavelet packet decomposition, the ship radiation signal is decomposed into different frequency bands, and its energy entropy feature is extracted. As for comparisons, the center frequency and permutation entropy are also used as features to be extracted, then the k-nearest neighbor is applied to classify and recognize the extracted results. Based on the comparisons of wavelet packet decomposition, the center frequency, permutation entropy, and the k-nearest neighbor are used for classification and recognition. The experimental results present that, when comparing with center frequency and permutation entropy, the method based on energy entropy has the best availability, with the highest average recognition rate for four types of ship radiation signals, up to 98%.
Highlights
In the development of science and technology, underwater acoustic signal processing plays an extremely important role in the human exploration of the ocean and plays a great role in military and marine life research [1, 2]
E train of thought of this paper is given as follows: Section 2 gives an introduction to wavelet packet decomposition; Section 3 proposes a feature extraction method based on permutation entropy (PE) and wavelet packet decomposition; Section 4 verifies the effectiveness of the feature extraction method proposed in this paper through comparative experiments; Section 5 embraces main conclusions obtained
Known as an optimal sub-band tree structure, is a further optimization of the wavelet transform. e main algorithm idea is on the basis of the wavelet transform, in each stage of signal decomposition, the low-frequency sub-band is further decomposed, and the high-frequency sub-band is further decomposed. e original signal is decomposed by the optimal signal decomposition path, which was calculated by minimizing a cost function
Summary
In the development of science and technology, underwater acoustic signal processing plays an extremely important role in the human exploration of the ocean and plays a great role in military and marine life research [1, 2]. Compared with approximate entropy and sample entropy, the arrangement is not affected by the length of the time series, and the calculation time is relatively short, so it has been applied in many fields [19,20,21]. As a preprocessor, wavelet packet decomposition can divide the frequency space into various finite frequency bands to realize the time-frequency localization of the signal. It can decompose the low frequency and the high frequency of the signal, and adaptively select the corresponding frequency band according to the analyzed signal, which has good timefrequency characteristics. E train of thought of this paper is given as follows: Section 2 gives an introduction to wavelet packet decomposition; Section 3 proposes a feature extraction method based on PE and wavelet packet decomposition; Section 4 verifies the effectiveness of the feature extraction method proposed in this paper through comparative experiments; Section 5 embraces main conclusions obtained
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
